added regular xgboost

regular_xgboost.py

@@ -0,0 +1,126 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+import pandas as pd
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import classification_report, confusion_matrix
+from sklearn.ensemble import GradientBoostingClassifier
+
+
+# In[4]:
+
+
+train_data = pd.read_csv(r"C:\Users\ankus\OneDrive\Desktop\regular\train.csv")
+test_data = pd.read_csv(r"C:\Users\ankus\OneDrive\Desktop\regular\test.csv")
+
+
+# In[5]:
+
+
+y_train = train_data["Survived"]
+train_data.drop(labels="Survived", axis=1, inplace=True)
+
+
+# In[6]:
+
+
+full_data = train_data.append(test_data)
+
+
+# In[7]:
+
+
+drop_columns = ["Name", "Age", "SibSp", "Ticket", "Cabin", "Parch", "Embarked"]
+full_data.drop(labels=drop_columns, axis=1, inplace=True)
+
+
+# In[8]:
+
+
+full_data = pd.get_dummies(full_data, columns=["Sex"])
+full_data.fillna(value=0.0, inplace=True)
+
+
+# In[9]:
+
+
+X_train = full_data.values[0:891]
+X_test = full_data.values[891:]
+
+
+# In[11]:
+
+
+scaler = MinMaxScaler()
+X_train = scaler.fit_transform(X_train)
+X_test = scaler.transform(X_test)
+#Now we can split the data into training and te
+
+
+# In[12]:
+
+
+state = 12  
+test_size = 0.30  
+
+X_train, X_val, y_train, y_val = train_test_split(X_train, y_train,  
+    test_size=test_size, random_state=state)
+
+
+# In[13]:
+
+
+lr_list = [0.05, 0.075, 0.1, 0.25, 0.5, 0.75, 1]
+
+for learning_rate in lr_list:
+    gb_clf = GradientBoostingClassifier(n_estimators=20, learning_rate=learning_rate, max_features=2, max_depth=2, random_state=0)
+    gb_clf.fit(X_train, y_train)
+
+    print("Learning rate: ", learning_rate)
+    print("Accuracy score (training): {0:.3f}".format(gb_clf.score(X_train, y_train)))
+    print("Accuracy score (validation): {0:.3f}".format(gb_clf.score(X_val, y_val)))
+
+
+# In[14]:
+
+
+gb_clf2 = GradientBoostingClassifier(n_estimators=20, learning_rate=0.5, max_features=2, max_depth=2, random_state=0)
+gb_clf2.fit(X_train, y_train)
+predictions = gb_clf2.predict(X_val)
+
+print("Confusion Matrix:")
+print(confusion_matrix(y_val, predictions))
+
+print("Classification Report")
+print(classification_report(y_val, predictions))
+
+
+# In[15]:
+
+
+from xgboost import XGBClassifier
+
+
+# In[16]:
+
+
+xgb_clf = XGBClassifier()
+xgb_clf.fit(X_train, y_train)
+
+
+# In[17]:
+
+
+score = xgb_clf.score(X_val, y_val)
+print(score)
+
+
+# In[ ]:
+
+
+
+